Overview
Value Iteration is a powerful algorithm in Reinforcement Learning that helps in determining the optimal policy for an agent. By iteratively applying the Bellman Equation, it updates the value of each state until the values stabilize, leading to the best possible actions in a given environment. This ...
Key Terms
Example: A robot navigating a maze.
Example: The maze in which the robot operates.
Example: Gaining points for reaching a goal.
Example: Choosing to move left or right in a maze.
Example: The current position of the robot in the maze.
Example: A discount factor of 0.9 means future rewards are valued at 90%.
Related Topics
Policy Iteration
An alternative method to find the optimal policy by evaluating and improving policies iteratively.
intermediateQ-Learning
A model-free reinforcement learning algorithm that learns the value of actions directly.
intermediateDeep Reinforcement Learning
Combines deep learning with reinforcement learning to handle complex environments.
advancedKey Concepts
Markov Decision ProcessBellman EquationOptimal PolicyConvergence